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COMET-QE and Active Learning for Low-Resource Machine Translation
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Active learning aims to deliver maximum benefit when resources are scarce. We use COMET-QE, a reference-free evaluation metric, to select sentences for low-resource neural machine translation. Using Swahili, Kinyarwanda and Spanish for our experiments, we show that COMET-QE significantly outperforms two variants of Round Trip Translation Likelihood (RTTL) and random sentence selection by up to 5 BLEU points for 20k sentences selected by Active Learning on a 30k baseline. This suggests that COMET-QE is a powerful tool for sentence selection in the very low-resource limit.
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ALPET: Active Few-shot Learning for Citation Worthiness Detection in Low-Resource Wikipedia Languages
ALPET, an active-learning plus PET pipeline, detects citation-worthy sentences in Catalan, Basque and Albanian while needing roughly 58-72% fewer labeled examples than its CCW baseline.
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